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Volume 45 Issue 4
Apr.  2023
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LI Yankai, XU Yuanyuan, LIU Ziqi, CHEN Yuqing. Aerial Infrared Target Detection Based on Improved YOLO v3 Algorithm[J]. Infrared Technology , 2023, 45(4): 386-393.
Citation: LI Yankai, XU Yuanyuan, LIU Ziqi, CHEN Yuqing. Aerial Infrared Target Detection Based on Improved YOLO v3 Algorithm[J]. Infrared Technology , 2023, 45(4): 386-393.
shu

Aerial Infrared Target Detection Based on Improved YOLO v3 Algorithm

  • Department of Logistics Engineering, Shanghai Maritime University, Shanghai 201306, China

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  • Received Date: May 02, 2022
  • Revised Date: June 07, 2022
    Graphical Abstract
    • Abstract
  • To further improve the performance of target detection under air combat conditions, a detection algorithm, namely EN-YOLO v3, based on an air infrared target and the optimization of YOLO v3, is proposed in this paper. The algorithm uses the lightweight EfficientNet backbone network as the backbone feature extraction network of YOLO v3 to reduce the number of model parameters and training time. Additionally, CIoU is used as the loss function of the model to optimize the model loss calculation method and improve its detection accuracy. The results show that compared with the original YOLO v3, the optimized EN-YOLO v3 target detection algorithm reduces the model size by 50.03% and improves the accuracy by 1.17%. This can effectively improve the detection of aerial targets in infrared scenes.
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    • References (20)
  • [1]
    秦鹏, 唐川明, 刘云峰, 等. 基于改进YOLO v3的红外目标检测方法[J]. 计算机工程, 2022, 48(3): 211-219. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202214010.htm

    QIN Peng, TANG Chuanming, LIU Yunfeng, et al. Infrared target detection method based on improved YOLO v3[J]. Computer Engineering, 2022, 48(3): 211-219. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202214010.htm
    [2]
    董小虎, 傅瑞罡, 高颖慧, 等. 复杂背景下红外小目标自适应检测跟踪[J]. 航空兵器, 2019, 26(6): 22-28. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906005.htm

    DONG Xiaohu, FU Ruigang, GAO Yinghui, et al. Detecting and tracking of small infrared targets in complex backgrounds[J]. Aero Weaponry, 2019, 26(6): 22-28. https://www.cnki.com.cn/Article/CJFDTOTAL-HKBQ201906005.htm
    [3]
    侯毅苇, 李林汉, 王彦. 结合红外显著性目标导引的改进YOLO网络的智能装备目标识别研究[J]. 红外技术, 2020, 42(7): 644-650. http://hwjs.nvir.cn/article/id/hwjs202007007

    HOU Yiwei, LI Linhan, WANG Yan. Research on intelligent equipment target recognition based on improved YOLO network combined with infrared saliency target guidance[J]. Infrared Technology, 2020, 42(7): 644-650. http://hwjs.nvir.cn/article/id/hwjs202007007
    [4]
    宋忠浩, 谷雨, 陈旭, 等. 基于加权策略的高分辨率遥感图像目标检测[J]. 计算机工程与应用, 2021, 57(13): 199-206. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202113025.htm

    SONG Zhonghao, GU Yu, CHEN Xu, et al. Object detection in high resolution remote sensing image based on weighting strategy[J]. Computer Engineering and Applications, 2021, 57(13): 199-206. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202113025.htm
    [5]
    Girshick R, Donahue J, Darrell T, et al. Rich feature Hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.
    [6]
    Girshick R. Fast R-CNN[C]//IEEE International Conference on Computer Vision, 2015: 1440-1448.
    [7]
    REN S, HE K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6): 1137-1149.
    [8]
    杨涛, 戴军, 吴钟建, 等. 基于深度学习的红外舰船目标识别[J]. 红外技术, 2020, 42(5): 426-433. http://hwjs.nvir.cn/article/id/hwjs202005003

    YANG Tao, DAI Jun, WU Zhangjian, et al. Infrared ship target recognition based on deep learning[J]. Infrared Technology, 2020, 42(5): 426-433. http://hwjs.nvir.cn/article/id/hwjs202005003
    [9]
    LIU W, Anguelov D, Erhan D, et al. SSD: Single Shot Multi Box Detector[C]//European Conference on Computer Vision, 2016: 21-37.
    [10]
    Redmon J, Divvala S, Girshick R, et al. You only look once: unified, real-time object detection[C]// IEEE Conference on Computer Vision & Pattern Recognition, 2016: 779-788.
    [11]
    杨鹏博. 基于深度学习的红外弱小目标检测与主动跟踪研究[D]. 上海: 中国科学院大学(中国科学院上海技术物理研究所), 2021.

    YANG Pengbo. Research on Infrared Weak and Small Target Detection and Active Tracking Based on Deep Learning[D]. Shanghai: University of Chinese Academy of Sciences(Shanghai Institute of Technical Physics Chinese Academy of Sciences), 2021.
    [12]
    李慕锴, 张涛, 崔文楠. 基于YOLO v3的红外行人小目标检测技术研究[J]. 红外技术, 2020, 42(2): 176-181. http://hwjs.nvir.cn/article/id/hwjs202002012

    LI Mukai, ZHANG Tao, CUI Wennan. Research on infrared pedestrian small target detection technology based on YOLO v3[J]. Infrared Technology, 2020, 42(2): 176-181. http://hwjs.nvir.cn/article/id/hwjs202002012
    [13]
    Redmon J, Farhadi A. YOLO V3: an incremental improvement [EB/OL]. [2023-04]. https://doi.org/10.48550/arXiv.1804.02767.
    [14]
    TAN M, LE Q V. Efficientnet: rethinking model scaling for convolutional neural networks[EB/OL]. [2023-04]. https://doi.org/10.48550/arXiv.1905.11946.
    [15]
    王振亚, 赵继红, 王艳鹏, 等. 基于改进EfficientNet网络的药片检测分类的研究[J]. 现代计算机, 2021, 27(28): 27-32. https://www.cnki.com.cn/Article/CJFDTOTAL-XDJS202128005.htm

    WANG Zhenya, ZHAO Jihong, WANG Yanpeng, et al. Research on detection and classification of pills based on improved EfficientNet network[J]. Modern Computer, 2021, 27(28): 27-32. https://www.cnki.com.cn/Article/CJFDTOTAL-XDJS202128005.htm
    [16]
    赵鹏菲, 黄丽佳. 一种基于EfficientNet与BiGRU的多角度SAR图像目标识别方法[J]. 雷达学报, 2021, 10(6): 895-904. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX202106009.htm

    ZHAO Pengfei, HUANG Lijia. A multi-angle SAR image target recognition method based on EfficientNet and BiGRU[J]. Journal of Radars, 2021, 10(6): 895-904. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX202106009.htm
    [17]
    许伟, 熊卫华, 姚杰, 等. 基于改进YOLO v3算法在垃圾检测上的应用[J]. 光电子·激光, 2020, 31(9): 928-938. https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ202001012.htm

    XU Wei, XIONG Weihua, YAO Jie, et al. Application of garbage detection based on improved YOLO v3 algorithm[J]. Journal of Optoelectronics·Laser, 2020, 31(9): 928-938. https://www.cnki.com.cn/Article/CJFDTOTAL-GDZJ202001012.htm
    [18]
    刘芳, 刘玉坤, 张白. 基于D-YOLOv3检测网络的温室叶菜幼苗图像检测[J]. 江苏农业学报, 2021, 37(5): 1262-1269. https://www.cnki.com.cn/Article/CJFDTOTAL-JSNB202105023.htm

    LIU Fang, LIU Yukun, ZHANG Bai. Image detection of greenhouse leafy vegetable seedlings based on D-YOLOv3 detection network[J]. Jiangsu Journal of Agricultural Sciences, 2021, 37(5): 1262-1269. https://www.cnki.com.cn/Article/CJFDTOTAL-JSNB202105023.htm
    [19]
    Rezatofighi H, Tsoi N, Gwak J Y, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 658-666.
    [20]
    ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//AAAI Conference on Artificial Intelligence, 2020: 12993-13000.
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